code: def main():
      data_root = args.data_root
      data_sets = args.data_sets
      num_workers = args.num_workers
  
      if args.log:
          logging.basicConfig(level=logging.INFO)
  
      if data_sets == "ALL":
          data_sets = "dev_clean,dev_other,train_clean_100,train_clean_360,train_other_500,test_clean,test_other"
      if data_sets == "mini":
          data_sets = "dev_clean_2,train_clean_5"
      for data_set in data_sets.split(","):
          logging.info("\n\nWorking on: {0}".format(data_set))
          filepath = os.path.join(data_root, data_set + ".tar.gz")
          logging.info("Getting {0}".format(data_set))
          __maybe_download_file(filepath, data_set.upper())
          logging.info("Extracting {0}".format(data_set))
          __extract_file(filepath, data_root)
          logging.info("Processing {0}".format(data_set))
          __process_data(
              os.path.join(
                  os.path.join(data_root, "LibriSpeech"),
                  data_set.replace("_", "-"),
              ),
              os.path.join(
                  os.path.join(data_root, "LibriSpeech"),
                  data_set.replace("_", "-"),
              )
              + "-processed",
              os.path.join(data_root, data_set + ".json"),
              num_workers=num_workers,
          )
      logging.info("Done!")
  
file_path: NeMo/scripts/dataset_processing/get_librispeech_data.py
imports: import argparse
  import fnmatch
  import functools
  import json
  import logging
  import multiprocessing
  import os
  import subprocess
  import tarfile
  import urllib.request
  from sox import Transformer
  from tqdm import tqdm
label_desc: Use image node_id 6 for calling a global function with example usage: main() without return types